In past decades, the association rules technology has be applied in data mining domain. The association rules algorithm has two parts. The first part is finding the frequent item set where purchase of times over support threshold from transaction data. The second part is finding the association rules from frequent item set.
In the association rules algorithm, the first part is time-consuming. Apriori algorithm is the most often used association rules algorithm in former association rules algorithm. Although, this algorithm can finding the frequent item set. But it has two shortcomings. The first shortcoming is generating candidate item set too much. The second shortcoming is scanning transaction data times without number. Therefore give occasion to time-consuming. Many experts propose the improvement ways in view of these two shortcomings. However the improvement ways are still using Apriori algorithm structure. In this paper we propose predictive association rules algorithm. This algorithm can finding the frequent item set quickly.
Predictive association rules algorithm only need scan the database two times. First scan finish length-Item distribute total table. Then using the length error number and frequent error number predictive all frequent item set. Finally scan the transaction finding real frequent item set.
